Application status and prospect of artificial intelligence in surgical treatment of primary liver cancer

Hongzhi LIU; Jingfeng LIU

doi:10.3969/j.issn.1001-5256.2022.01.001

Volume 38 Issue 1

Jan. 2022

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Article Navigation > Journal of Clinical Hepatology > 2022 > 38(1): 10-14

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Application status and prospect of artificial intelligence in surgical treatment of primary liver cancer

DOI: 10.3969/j.issn.1001-5256.2022.01.001

Hongzhi LIU¹,
Jingfeng LIU^{1, 2
,
,}

1.
Southeast Big Data Institute of Hepatobiliary Health, Mengchao Hepatobiliary Hospital of Fujian Medical University, Fuzhou 350025, China
2.
Department of Hepatopancreatobiliary Tumor Surgery, Fujian Provincial Tumor Hospital, Fuzhou 350014, China

Research funding:

Specific Foundation of Development and Reform Commission in Fujian Province (31010308);

Science and Technology Innovation Platform Project of Fuzhou Science and Technology Bureau (2021-P-055)

Received Date: 2021-11-02
Accepted Date: 2021-11-02
Published Date: 2022-01-20

Abstract

Abstract

Primary liver cancer is one of the most common and fatal malignant tumors, and surgical treatment is the most important radical treatment method, but there is still a high postoperative recurrence rate and poor prognosis. In recent years, emerging techniques represented by artificial intelligence have achieved rapid innovation and are gradually integrated into the whole process of the diagnosis and treatment of primary liver cancer. Promoting the implementation of artificial intelligence in the surgical treatment of primary liver cancer is of great significance to the high-quality development of precision liver surgery. At present, researchers have extensively explored the application of artificial intelligence in treatment decision-making, preoperative evaluation, surgical implementation, postoperative management, and adjuvant therapy for primary liver cancer. This article reviews the advances in the application of artificial intelligence in the surgical treatment of primary liver cancer, so as to accelerate the application of artificial intelligence in clinical diagnosis and treatment, improve clinical service ability, and ultimately improve patients' prognosis.
- Liver Neoplasm,
- Artificial Intelligence,
- Surgical Procedures, Operative

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References(46)

References

[1]	SUNG H, FERLAY J, SIEGEL RL, et al. Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries[J]. CA Cancer J Clin, 2021, 71(3): 209-249. DOI: 10.3322/caac.21660.
[2]	DHIR M, MELIN AA, DOUAIHER J, et al. A review and update of treatment options and controversies in the management of hepatocellular carcinoma[J]. Ann Surg, 2016, 263(6): 1112-1125. DOI: 10.1097/SLA.0000000000001556.
[3]	LIU JF, LIU HZ, CHEN ZW, et al. Construction system and preliminary application of big data platform for liver disease and liver cancer[J]. Chin J Dig Surg, 2021, 20(1): 46-51. DOI: 10.3760/cma.j.cn115610-20201126-00742. 刘景丰, 刘红枝, 陈振伟, 等. 肝病和肝癌大数据平台建设体系及其初步应用[J]. 中华消化外科杂志, 2021, 20(1): 46-51. DOI: 10.3760/cma.j.cn115610-20201126-00742.
[4]	European Association for the Study of the Liver. EASL clinical practice guidelines: Management of hepatocellular carcinoma[J]. J Hepatol, 2018, 69(1): 182-236. DOI: 10.1016/j.jhep.2018.03.019.
[5]	CHOI GH, YUN J, CHOI J, et al. Development of machine learning-based clinical decision support system for hepatocellular carcinoma[J]. Sci Rep, 2020, 10(1): 14855. DOI: 10.1038/s41598-020-71796-z.
[6]	LIU F, LIU D, WANG K, et al. Deep learning radiomics based on contrast-enhanced ultrasound might optimize curative treatments for very-early or early-stage hepatocellular carcinoma patients[J]. Liver Cancer, 2020, 9(4): 397-413. DOI: 10.1159/000505694.
[7]	FU S, WEI J, ZHANG J, et al. Selection between liver resection versus transarterial chemoembolization in hepatocellular carcinoma: A multicenter study[J]. Clin Transl Gastroenterol, 2019, 10(8): e00070. DOI: 10.14309/ctg.0000000000000070.
[8]	FANG CH, ZHANG P, ZHOU WP, et al. Efficacy of three-dimensional visualization technology in the precision diagnosis and treatment for primary liver cancer: A retrospective multicenter study of 1 665 cases in China[J]. Chin J Surg, 2020, 58(5): 375-382. DOI: 10.3760/cma.j.cn112139-20200220-00105. 方驰华, 张鹏, 周伟平, 等. 三维可视化技术用于1665例原发性肝癌精准诊治的多中心回顾性研究[J]. 中华外科杂志, 2020, 58(5): 375-382. DOI: 10.3760/cma.j.cn112139-20200220-00105.
[9]	Chinese Society of Digital Medicine, Chinese Medical Association; Professional Committee of Digital Intelligent Surgery, Chinese Research Hospital Association; Chinese Society of Liver Cancer, Chinese Medical Doctor Association; et al. Clinical practice guidelines for precision diagnosis and treatment of complex liver tumor guided by three-dimensional visualization technology (version 2019)[J]. Chin J Pract Surg, 2019, 39(8): 11-19. DOI: 10.12122/j.issn.1673-4254.2020.03.01.. 中华医学会数字医学分会, 中国研究型医院学会数字智能化外科专业委员会, 中国医师协会肝癌专业委员会, 等. 复杂性肝脏肿瘤切除三维可视化精准诊治指南(2019版)[J]. 中国实用外科杂志, 2019, 39(8): 11-19. DOI: 10.12122/j.issn.1673-4254.2020.03.01.
[10]	XIE LT, GU JH, CHAI WL, et al. Pre-operative detection of liver fibrosis in hepatocellular carcinoma patients using 2D shear wave elastography: Where to measure?[J]. Ultrasound Med Biol, 2020, 46(6): 1412-1423. DOI: 10.1016/j.ultrasmedbio.2020.02.012.
[11]	LEE JH, JOO I, KANG TW, et al. Deep learning with ultrasonography: Automated classification of liver fibrosis using a deep convolutional neural network[J]. Eur Radiol, 2020, 30(2): 1264-1273. DOI: 10.1007/s00330-019-06407-1.
[12]	WANG K, LU X, ZHOU H, et al. Deep learning Radiomics of shear wave elastography significantly improved diagnostic performance for assessing liver fibrosis in chronic hepatitis B: A prospective multicentre study[J]. Gut, 2019, 68(4): 729-741. DOI: 10.1136/gutjnl-2018-316204.
[13]	WEI J, JIANG H, GU D, et al. Radiomics in liver diseases: Current progress and future opportunities[J]. Liver Int, 2020, 40(9): 2050-2063. DOI: 10.1111/liv.14555.
[14]	CHEN W, ZHANG T, XU L, et al. Radiomics analysis of contrast-enhanced CT for hepatocellular carcinoma grading[J]. Front Oncol, 2021, (11): 660509. DOI: 10.3389/fonc.2021.660509.
[15]	ZHOU W, WANG G, XIE G, et al. Grading of hepatocellular carcinoma based on diffusion weighted images with multiple b-values using convolutional neural networks[J]. Med Phys, 2019, 46(9): 3951-3960. DOI: 10.1002/mp.13642.
[16]	ZHOU W, JIAN W, CEN X, et al. Prediction of microvascular invasion of hepatocellular carcinoma based on contrast-enhanced MR and 3D convolutional neural networks[J]. Front Oncol, 2021, 11: 588010. DOI: 10.3389/fonc.2021.588010.
[17]	JIANG YQ, CAO SE, CAO S, et al. Preoperative identification of microvascular invasion in hepatocellular carcinoma by XGBoost and deep learning[J]. J Cancer Res Clin Oncol, 2021, 147(3): 821-833. DOI: 10.1007/s00432-020-03366-9.
[18]	ZHOU W, JIAN W, CEN X, et al. Prediction of microvascular invasion of hepatocellular carcinoma based on contrast-enhanced MR and 3D convolutional neural networks[J]. Front Oncol, 2021, 11: 588010. DOI: 10.3389/fonc.2021.588010.
[19]	CHEN S, FENG S, WEI J, et al. Pretreatment prediction of immunoscore in hepatocellular cancer: A radiomics-based clinical model based on Gd-EOB-DTPA-enhanced MRI imaging[J]. Eur Radiol, 2019, 29(8): 4177-4187. DOI: 10.1007/s00330-018-5986-x.
[20]	GU D, XIE Y, WEI J, et al. MRI-based radiomics signature: A potential biomarker for identifying glypican 3-positive hepatocellular carcinoma[J]. J Magn Reson Imaging, 2020, 52(6): 1679-1687. DOI: 10.1002/jmri.27199.
[21]	CHIU CC, LEE KT, LEE HH, et al. Comparison of models for predicting quality of life after surgical resection of hepatocellular carcinoma: A prospective study[J]. J Gastrointest Surg, 2018, 22(10): 1724-1731. DOI: 10.1007/s11605-018-3833-7.
[22]	XU Y, CHEN M, MENG X, et al. Laparoscopic anatomical liver resection guided by real-time indocyanine green fluorescence imaging: Experience and lessons learned from the initial series in a single center[J]. Surg Endosc, 2020, 34(10): 4683-4691. DOI: 10.1007/s00464-020-07691-5.
[23]	LI J, LI X, ZHANG X, et al. Indocyanine green fluorescence imaging-guided laparoscopic right posterior hepatectomy[J]. Surg Endosc, 2021. DOI: 10. 1007/s00464-021-08404-2.[Epub ahead of print]
[24]	BERTRAND LR, ABDALLAH M, ESPINEL Y, et al. A case series study of augmented reality in laparoscopic liver resection with a deformable preoperative model[J]. Surg Endosc, 2020, 34(12): 5642-5648. DOI: 10.1007/s00464-020-07815-x.
[25]	ZOU H, ZHU CZ, WANG C, et al. Application of three-dimensional reconstruction combined with augmented reality technology in precise hepatectomy performed by robot[J]. Chin J Robotic Surg, 2020, 1(2): 141-147. DOI: 10.12180/j.issn.2096-7721.2020.02.009. 邹浩, 朱呈瞻, 王畅, 等. 三维重建结合增强现实技术在机器人下精准肝切除术中的应用[J]. 机器人外科学杂志, 2020, 1(2): 141-147. DOI: 10.12180/j.issn.2096-7721.2020.02.009.
[26]	ZHANG W, ZHU W, YANG J, et al. Augmented reality navigation for stereoscopic laparoscopic anatomical hepatectomy of primary liver cancer: Preliminary experience[J]. Front Oncol, 2021, 11: 663236. DOI: 10.3389/fonc.2021.663236.
[27]	GIORDANO S, TAKEDA S, DONADON M, et al. Rapid automated diagnosis of primary hepatic tumour by mass spectrometry and artificial intelligence[J]. Liver Int, 2020, 40(12): 3117-3124. DOI: 10.1111/liv.14604.
[28]	MERATH K, HYER JM, MEHTA R, et al. Use of machine learning for prediction of patient risk of postoperative complications after liver, pancreatic, and colorectal surgery[J]. J Gastrointest Surg, 2020, 24(8): 1843-1851. DOI: 10.1007/s11605-019-04338-2.
[29]	LIN KC, LIU JF, ZENG JH, et al. Value of a virtual liver surgery planning system in predicting hepatic dysfunction after hepatectomy for liver cancer[J]. Chin J Dig Surg, 2012, 11(2): 116-119. DOI: 10.3760/cma.j.issn.1673-9752.2012.02.007. 林科灿, 刘景丰, 曾金华, 等. 虚拟肝脏手术规划系统预测肝癌肝切除术后肝功能损害的价值[J]. 中华消化外科杂志, 2012, 11(2): 116-119. DOI: 10.3760/cma.j.issn.1673-9752.2012.02.007.
[30]	CAI W, HE B, HU M, et al. A radiomics-based nomogram for the preoperative prediction of posthepatectomy liver failure in patients with hepatocellular carcinoma[J]. Surg Oncol, 2019, 28: 78-85. DOI: 10.1016/j.suronc.2018.11.013.
[31]	ZHU WS, SHI SY, YANG ZH, et al. Radiomics model based on preoperative gadoxetic acid-enhanced MRI for predicting liver failure[J]. World J Gastroenterol, 2020, 26(11): 1208-1220. DOI:10. 748/wjg.v26.i11.1208.
[32]	MAI RY, LU HZ, BAI T, et al. Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma[J]. Surgery, 2020, 168(4): 643-652. DOI: 10.1016/j.surg.2020.06.031.
[33]	JI GW, ZHU FP, XU Q, et al. Machine-learning analysis of contrast-enhanced CT radiomics predicts recurrence of hepatocellular carcinoma after resection: A multi-institutional study[J]. EBioMedicine, 2019, 50: 156-165. DOI: 10.1016/j.ebiom.2019.10.057.
[34]	ZHANG Z, JIANG H, CHEN J, et al. Hepatocellular carcinoma: Radiomics nomogram on gadoxetic acid-enhanced MR imaging for early postoperative recurrence prediction[J]. Cancer Imaging, 2019, 19(1): 22. DOI: 10.1186/s40644-019-0209-5.
[35]	KIM S, SHIN J, KIM DY, et al. Radiomics on gadoxetic acid-enhanced magnetic resonance imaging for prediction of postoperative early and late recurrence of single hepatocellular carcinoma[J]. Clin Cancer Res, 2019, 25(13): 3847-3855. DOI: 10.1158/1078-0432.CCR-18-2861.
[36]	HUANG Y, CHEN H, ZENG Y, et al. Development and validation of a machine learning prognostic model for hepatocellular carcinoma recurrence after surgical resection[J]. Front Oncol, 2020, 10: 593741. DOI: 10.3389/fonc.2020.593741.
[37]	SHINKAWA H, TANAKA S, TAKEMURA S, et al. Nomograms predicting extra- and early intrahepatic recurrence after hepatic resection of hepatocellular carcinoma[J]. Surgery, 2021, 169(4): 922-928. DOI: 10.1016/j.surg.2020.10.012.
[38]	CHEN Y, ZENG J, GUO P, et al. Prognostic significance of platelet-to-lymphocyte ratio (PLR) in extrahepatic metastasis of hepatocellular carcinoma after curative resection[J]. Cancer Manag Res, 2021, 13: 1395-1405. DOI: 10.2147/CMAR.S290738.
[39]	FU S, PAN M, ZHANG J, et al. Deep learning-based prediction of future extrahepatic metastasis and macrovascular invasion in hepatocellular carcinoma[J]. J Hepatocell Carcinoma, 2021, 8: 1065-1076. DOI: 10.2147/JHC.S319639.
[40]	ZHANG XP, GAO YZ, CHEN ZH, et al. An eastern hepatobiliary surgery hospital/portal vein tumor thrombus scoring system as an aid to decision making on hepatectomy for hepatocellular carcinoma patients with portal vein tumor thrombus: A multicenter study[J]. Hepatology, 2019, 69(5): 2076-2090. DOI: 10.1002/hep.30490.
[41]	ZHANG XP, WANG K, GAO YZ, et al. Prognostic model for identifying candidates for hepatectomy among patients with hepatocellular carcinoma and hepatic vein invasion[J]. Br J Surg, 2020, 107(7): 865-877. DOI: 10.1002/bjs.11524.
[42]	TSILIMIGRAS DI, MEHTA R, PAREDES AZ, et al. Overall tumor burden dictates outcomes for patients undergoing resection of multinodular hepatocellular carcinoma beyond the milan criteria[J]. Ann Surg, 2020, 272(4): 574-581. DOI: 10.1097/SLA.0000000000004346.
[43]	ZENG J, LIN K, LIU H, et al. Prognosis factors of young patients undergoing curative resection for hepatitis B virus-related hepatocellular carcinoma: A multicenter study[J]. Cancer Manag Res, 2020, 12: 6597-6606. DOI: 10.2147/CMAR.S261368.
[44]	LIN K, HUANG Q, WANG L, et al. Pre- and postoperative models for prediction of recurrence in non-B, non-C hepatocellular carcinoma[J]. Front Oncol, 2021, 11: 612588. DOI: 10.3389/fonc.2021.612588.
[45]	LIN K, HUANG Q, ZENG J, et al. Clinical significance of alpha-fetoprotein in alpha-fetoprotein negative hepatocellular carcinoma underwent curative resection[J]. Dig Dis Sci, 2021, 66(12): 4545-4556. DOI: 10.1007/s10620-020-06797-z.
[46]	LI Y, XIA Y, LI J, et al. Prognostic Nomograms for pre- and postoperative predictions of long-term survival for patients who underwent liver resection for huge hepatocellular carcinoma[J]. J Am Coll Surg, 2015, 221(5): 962-974. e4. DOI: 10.1016/j.jamcollsurg.2015.08.003.

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Application status and prospect of artificial intelligence in surgical treatment of primary liver cancer

DOI: 10.3969/j.issn.1001-5256.2022.01.001

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Application status and prospect of artificial intelligence in surgical treatment of primary liver cancer

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